Let’s dive into the different types of plots you can create using Matplotlib, along with examples:
Bar Chart:
A bar chart (or bar plot) displays categorical data with rectangular bars. Each bar represents a category, and the height of the bar corresponds to the value of that category.
Example:
import matplotlib.pyplot as plt
x = [1, 3, 5, 7, 9]
y1 = [5, 2, 7, 8, 2]
y2 = [8, 6, 2, 5, 6]
plt.bar(x, y1, label=“Example one“)
plt.bar(x, y2, label=“Example two“, color=‘g‘)
plt.xlabel(“X-axis“)
plt.ylabel(“Y-axis“)
plt.title(“Bar Chart Example“)
plt.legend()
plt.show()
This code creates a bar chart with two sets of bars, labeled “Example one” and “Example two” ⁴.
2.Histogram:
A histogram represents the distribution of continuous data by dividing it into bins and showing the frequency of values falling into each bin.
Example:
import matplotlib.pyplot as plt
import numpy as np
data = np.random.randn(1000) # Generate random data
plt.hist(data, bins=20, edgecolor=‘black‘)
plt.xlabel(“Value“)
plt.ylabel(“Frequency“)
plt.title(“Histogram Example“)
plt.show()
This code creates a histogram from random data.
3.Scatter Plot:
A scatter plot displays individual data points as dots. It is useful for visualizing relationships between two continuous variables.
Example:
import matplotlib.pyplot as plt
x = [10, 20, 30, 40]
y = [20, 25, 35, 55]
plt.scatter(x, y, marker=‘o‘, color=‘b‘, label=“Data points“)
plt.xlabel(“X-axis“)
plt.ylabel(“Y-axis“)
plt.title(“Scatter Plot Example“)
plt.legend()
plt.show()
This code creates a scatter plot with data points.
4.Pie Chart:
A pie chart represents parts of a whole. It shows the proportion of each category relative to the total.
Example:
import matplotlib.pyplot as plt
labels = [‘Apples‘, ‘Bananas‘, ‘Cherries‘, ‘Dates‘]
sizes = [30, 25, 20, 15]
plt.pie(sizes, labels=labels, autopct=‘%1.1f%%‘, startangle=90)
plt.axis(‘equal‘) # Equal aspect ratio ensures a circular pie chart
plt.title(“Pie Chart Example“)
plt.show()
This code creates a simple pie chart with labeled slices ².
Remember to customize these plots further by adjusting labels, colors, and other parameters according to your specific requirements. Happy plotting! 😊📊🎨
